CN113656466A - Policy data query method, device, equipment and storage medium - Google Patents
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Abstract
The application relates to a data processing technology, and provides a policy data query method, a policy data query device, policy data query equipment and a computer-readable storage medium, wherein the policy data query method comprises the following steps: acquiring task information sent by a client; based on a task management system, determining a policy corresponding to the task information according to the task information; when the number of the policy corresponding to the task information is greater than or equal to a preset value, determining common policy data according to policy data in all the policies corresponding to the task information, wherein the common policy data is policy data at least existing in two policies, and the preset value is a natural number greater than 1; establishing a data association node according to the common policy data based on a node establishment model; in response to a data acquisition instruction, searching target data in the data association node; and sending the target data to the client. The application also relates to a block chain technology, and a policy corresponding to the task information can be stored in the block chain.
Description
Technical Field
The present application relates to the field of data query technologies, and in particular, to a policy data query method, apparatus, device, and computer-readable storage medium.
Background
At present, in some insurance services, a plurality of policy bundles exist, in this case, if one policy data needs to be searched, such as the amount of the policy, the policy data needs to be queried in a plurality of policies, the data processing amount is large, more resources are needed, and the query efficiency is not high.
Disclosure of Invention
The present application mainly aims to provide a policy data query method, device, equipment and computer readable storage medium, aiming to improve the policy data query efficiency.
In a first aspect, the present application provides a policy data query method, including the steps of:
acquiring task information sent by a client;
based on a task management system, determining a policy corresponding to the task information according to the task information;
when the number of the policy corresponding to the task information is greater than or equal to a preset value, determining common policy data according to policy data in all the policies corresponding to the task information, wherein the common policy data is policy data at least existing in two policies, and the preset value is a natural number greater than 1;
establishing a data association node according to the common policy data based on a node establishment model;
in response to a data acquisition instruction, searching target data in the data association node;
and sending the target data to the client.
In a second aspect, the present application further provides a policy data query apparatus, including:
the task information acquisition module is used for acquiring task information sent by the client;
the policy determining module is used for determining a policy corresponding to the task information according to the task information based on the task management system;
a common policy data determining module, configured to determine common policy data according to policy data in all policies corresponding to the task information when the number of the policies corresponding to the task information is greater than or equal to a preset value, where the common policy data is policy data existing in at least two policies, and the preset value is a natural number greater than 1;
the data association node establishing module is used for establishing a data association node according to the common policy data based on a node establishing model;
the target data searching module is used for responding to a data acquisition instruction and searching target data in the data correlation node;
and the target data output module is used for sending the target data to the client.
In a third aspect, the present application also provides a computer device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, implements the steps of the policy data query method as described above.
In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, where the computer program, when executed by a processor, implements the steps of the policy data query method as described above.
The application provides a policy data query method, a policy data query device, policy data query equipment and a computer readable storage medium, wherein the policy data query method comprises the following steps: acquiring task information sent by a client; based on a task management system, determining a policy corresponding to the task information according to the task information; when the number of the policy corresponding to the task information is greater than or equal to a preset value, determining common policy data according to policy data in all the policies corresponding to the task information, wherein the common policy data is policy data at least existing in two policies, and the preset value is a natural number greater than 1; establishing a data association node according to the common policy data based on a node establishment model; in response to a data acquisition instruction, searching target data in the data association node; and sending the target data to the client. The method and the system for processing the insurance policy data have the advantages that the number of the insurance policies corresponding to the task information is determined through the task management system, when the number of the insurance policies corresponding to the task information is larger than or equal to the preset value, the common insurance policy data are extracted, the associated nodes are established, data searching is carried out in the associated nodes, when the insurance policy data are searched, each insurance policy is not needed to be searched and modified, data processing time is effectively shortened, and burden of computer data processing is relieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a policy data query method according to an embodiment of the present application;
fig. 2 is a schematic view of a scenario for implementing the policy data query method provided in this embodiment;
FIG. 3 is a schematic block diagram of a policy data query device according to an embodiment of the present application;
fig. 4 is a block diagram illustrating a structure of a computer device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The flow diagrams depicted in the figures are merely illustrative and do not necessarily include all of the elements and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The embodiment of the application provides a policy data query method, a policy data query device, computer equipment and a computer readable storage medium. The policy data query method can be applied to terminal equipment, and the terminal equipment can be electronic equipment such as a tablet computer, a notebook computer and a desktop computer. The method can also be applied to a server, which can be an independent server, or a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, Content Delivery Network (CDN), big data and artificial intelligence platform, and the like.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a policy data query method according to an embodiment of the present application.
As shown in fig. 1, the policy data query method includes steps S101 to S106.
And step S101, acquiring task information sent by the client.
For example, task information sent by the client is obtained, where the task information may be policy information or information of an insurance task, in some insurance tasks, an insurer and/or a user may input the task information on an app of the terminal device, that is, on the app of the client, and after the client obtains the information input by the insurer and/or the user, the client generates the task information and transmits the task information to the server and/or the service terminal, so that the server and/or the service terminal can obtain the task information.
Specifically, the task information may include a task serial number, a task topic, task content, and the like, where the task serial number may be formed by characters, and the task topic and the task content are used to indicate which client and/or under which circumstances the task is used, for example, company a cannot continue to operate due to machine damage, and in this case, the task topic may include machine damage risk and business interruption risk. The task information is merely an example, and specific contents of the task information are not limited.
And S102, determining a policy corresponding to the task information according to the task information based on a task management system.
For example, the task information may be input into the task management system to obtain the policy corresponding to the task information in the task management system, so as to determine the policy number of the current task corresponding to the task information.
For example, the policy corresponding to the task may be stored in the blockchain, that is, the policy of the task may be stored in a blockchain manner by the task management system, after the service end obtains the task information, the service end broadcasts the task information to the blockchain network, the task information may include a task keyword, a task topic, and/or a task sequence number extracted from the task information, and in the blockchain network, the storage address of the task may be determined by the task keyword, the task topic, and/or the task sequence number.
In some embodiments, the method further comprises: extracting a plurality of task keywords from the task information based on a keyword extraction model; the task-based management system determines the number of insurance policies corresponding to the task information according to the task information, and comprises the following steps: and determining the number of insurance policies corresponding to the task in the task management system according to the task keywords.
For example, a plurality of task keywords may be extracted from the task information, so as to determine the number of policies corresponding to the task information in the task management system according to the task keywords. It can be understood that the task keywords extracted from the task information may be descriptions of task conditions written when the user reports the task information.
For example, the task keyword may include "loss of mobile phone", "car insurance", "business interruption", and the like, and when the above-mentioned character is detected, the characters located near the above-mentioned character, such as the characters in the front and back 5 positions, may be obtained at the same time, or the first punctuation mark is detected forward and the second punctuation mark is detected backward, and all the characters located in the first punctuation mark and the second punctuation mark are extracted to obtain the task keyword.
Illustratively, keywords may be extracted for the task exception information based on a keyword extraction model. The keyword extraction model can be obtained by training the neural network model according to the labeled keyword data, and the parameters of the neural network model can be obtained by learning and adjusting from the labeled keyword data based on an algorithm framework of online machine learning.
For example, the labeled keyword data may include keyword data of a common corpus and/or a business corpus, wherein the common corpus is, for example, open-source corpus participle data, and the business corpus data may be business corpus participle data stored on the process management system.
Illustratively, the extraction of the keywords may be performed on the task exception information based on a keyword extraction model and sequence labeling of the words. For the word sequence of the input task abnormal information, the keyword extraction model can mark a mark for identifying a word boundary for each word in the task abnormal information, and the task keywords in the task abnormal information can be determined according to the mark for identifying the word boundary.
Illustratively, the extraction of the keywords may also be performed on the task exception information based on the keyword extraction model and the labeled keyword data. For the acquired task abnormal information, the keyword extraction model can compare the task abnormal information with the labeled keyword data, and according to the comparison result, the same or similar phrases are determined as the task keywords in the task abnormal information.
When the task identifier is broadcast to the blockchain network, the blockchain network can determine the mapping relation with the policy storage address according to the task keyword, the task topic and/or the task serial number to obtain the storage address. The storage address is used to indicate the storage location of the policy in the block chain, such as on a block of the block chain; the block chain network knows the storage position of the policy to be called by the current server in the block chain according to the storage address, and can find the corresponding block of the block chain to extract the policy required by the server, so that the number of the policies is determined through the extracted policy.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism and an encryption algorithm. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Illustratively, the rate of calculating the number of the policy is effectively improved by determining the number of the policy corresponding to the task in the task management system through the task key words, the task theme and/or the task serial number.
In some embodiments, the determining, in the task management system according to the task keyword, the number of policies corresponding to the task includes: traversing all tasks in the task management system according to the task keywords, and determining the matching degree of the task keywords and each task in the task management system; determining a target task in the task management system according to the matching degree; and determining the number of the policy corresponding to the target task according to a preset mapping relation.
Illustratively, the task keywords are traversed through all the tasks in the task management system to determine the matching degree of the task keywords and each task in the task management system, and it can be understood that the matching degree can be determined by the same number of characters contained in the task keywords and each task or the policy corresponding to each task in the task management system, and the matching degree is positively correlated with the same number of characters.
For example, a matching degree threshold may be preset, and the task with the matching degree higher than the matching degree threshold may be determined as the target task by comparing the matching degree threshold with the matching degree.
For example, the preset matching degree threshold may be set to 10, 5 keywords in the task keywords are the same as 5 words in the policy corresponding to a certain task, it is determined that the matching degree of the task keywords and the task is 5, and if the matching degree is smaller than the matching degree threshold, the task is not the target task, and the policy corresponding to the task is not the task corresponding to the task keywords; if 15 keywords of the task keywords are the same as 15 words of the policy corresponding to a certain task, the matching degree of the task keywords and the task is determined to be 15, and the matching degree is greater than the threshold value of the matching degree, so that the task can be determined to be the target task.
For example, the target task may be determined by comparing the matching degree of each task in the task management system with the task keyword, for example, the task with the highest matching degree is determined as the target task.
Illustratively, in the task management system, a mapping relationship exists between tasks and policy, for example, the A task corresponds to a policy and B policy, and the B task corresponds to c policy, d policy and e policy.
Illustratively, after the target task is determined, the policy corresponding to the target task is obtained based on the mapping relationship between the target task and the policy in the task management system, so as to determine the policy quantity corresponding to the target task.
The target task is determined in the task management system through the task keywords, so that the target task can be effectively determined, and the wrong selection of the target task is reduced.
And S103, when the number of the policy corresponding to the task information is greater than or equal to a preset value, determining common policy data according to policy data in all the policies corresponding to the task information, wherein the common policy data is policy data at least existing in two policies, and the preset value is a natural number greater than 1.
For example, when the number of the policies corresponding to the task information is greater than or equal to a preset value, policy data in all the policies corresponding to the task are obtained, for example, when the task corresponds to two policies, all the policy data in the two policies are obtained. As will be appreciated, policy data may include items to be guaranteed, time of validity, premium, and the like.
For example, the same policy data, such as the same item being insured or the same premium, may appear in both policies, with the same policy data being determined as common policy data in both policies.
In some embodiments, the determining common policy data according to policy data in all policies corresponding to the task information includes: traversing policy data in all policies to obtain a key value pair corresponding to each policy data; and if the corresponding key value pairs of the policy data in the multiple policies are the same, determining the policy data in the multiple policies as common policy data.
For example, the policy data may include a plurality of data items, such as a premium item, an effective time item, and the like, the data items may be stored in the policy of the task management system in the form of key-value pairs, and each policy data item corresponds to one key-value pair, and the key-value pairs in the policy may be traversed to obtain policy data in the policy and determine whether common policy data exists in the policy data.
Illustratively, the policy data items may be stored in the policy of the task management system in the form of Key-Value pairs (Key-Value), and each policy data item corresponds to one Key-Value pair, where Key is fixed as a field and is represented by a field object, and Value may be any one of a field, a list, a hash, a set, and an ordered set object, and all policy data in the policy is traversed to obtain the Key-Value pair of each policy data.
And comparing the key value pairs of the policy data items in different policies one by one through a comparison network, determining the policy data items corresponding to the same key value pairs, and determining the policy data items as policy data common to the different policies.
For example, the policy data in the policy a includes 3 policy data items, the policy data items correspond to key value pairs of e, c, d, respectively, and the policy data in the policy B includes 5 policy data items, the corresponding key value pairs are a, B, c, d, e, respectively, through comparing the policy data items in the policy a with a, B, c, d, e one by one, it can be understood that e in the policy a is different from any one of a, B, c, d, e in the policy B, the policy data item corresponding to e is not the common policy data of A, B policy, it can be understood that c, d in the policy a is the same as one of a, B, c, d, e, and the policy data item corresponding to B, c is the common policy data of A, B policy.
For example, when all key-value pairs corresponding to policy data in the a policy are the same as all key-value pairs corresponding to policy data in the B policy, policy data in the a policy and the B policy may be determined as common policy data.
And S104, establishing a data association node according to the common policy data based on the node establishing model.
Illustratively, based on the node establishment model, data association nodes are established for common policy data existing in different policies, and it can be understood that the data association nodes may add special identifiers to key value pairs corresponding to the common policy data, so that policy data including the special identifiers can be known as the common policy data according to the special identifiers. It will be appreciated that the particular identity to which different common policy data is added may vary, for example, common policy data in the A, B policy may be added with an "A-B" identity and common policy data in the B, C policy may be added with a "B-C" identity.
For example, the node establishment model may be further configured to extract common policy data, and store the extracted common policy data in an associated data set, and it may be understood that the associated data set may serve as a data association node, and common data in different policies may also be stored in different associated data sets, so as to distinguish common policy data between different policies, for example, common policy data of an a policy and a B policy is stored in the associated data set No. 1, and common policy data of a B policy and a C policy is stored in the associated data set No. 2.
Illustratively, the data association node is established for the common policy data through the node establishment model, so that the data can be searched based on the established data association node when the target data is searched, and the data searching speed and accuracy are improved.
In some embodiments, the establishing a data association node for the common policy data based on the node establishment model includes: determining an associated identifier corresponding to the common policy data based on an identifier network in the node establishment model; and classifying the common policy data into at least one associated data group according to the associated identification so as to complete the establishment of the data associated node.
For example, based on the identifier network of the node establishment model, the association identifier corresponding to the common policy data is determined, where the association identifier may be an identifier in a key value pair of the common policy data, as described above.
Illustratively, a plurality of common policy data is classified into at least one associated data set according to different associated identifications, and it is understood that there is at least one common policy data in each associated data.
For example, common policy data for two policies may be categorized into one associated data set, and common policy data for three policies may be categorized into another associated data set; alternatively, the common policy data associated with the premium is categorized in one associated data set and the common policy data associated with the item being insured is categorized in another associated data set. When the premium or the guaranteed items are respectively inquired, the inquiry time can be effectively prolonged.
And S105, responding to the data acquisition instruction, and searching target data in the data correlation node.
For example, the server may search for the target data in the multiple data association nodes in response to the data acquisition instruction, and it can be understood that searching for the target data in the data association nodes may effectively increase a data search rate, and may not involve too much data in the search process, thereby reducing a data burden of the computer. Specifically, the target data may be searched for in a plurality of associated data sets.
For example, the server may obtain a data obtaining instruction from the client, for example, after the task information is obtained from the client, the server determines whether the number of policy corresponding to the task information is greater than or equal to a preset value according to the task information, if so, determines common policy data from a plurality of policies corresponding to the task, and after a data association node is established for the common policy data, the server obtains a data obtaining instruction generated by the client in response to an input of a user/an insurer, where the data obtaining instruction includes an instruction for instructing to obtain target data. It will be appreciated that the target data may be one or more of a guaranteed item, a validity time, a premium.
In some embodiments, the searching for target data in the data association node in response to the data obtaining instruction includes: acquiring the identification of the target data in the data acquisition instruction; and determining a target data group in the plurality of associated data groups according to the identification of the target data, and searching the target data in the target data group.
For example, the data acquisition instruction may include an identification of the target data, the identification may be, for example, a fee, a time, the fee may be used to indicate that the target data to be acquired is a premium or a commission fee, and the like.
For example, when the common policy data is classified according to the association identifier, the common policy data may be classified into a cost data group, a duration data group, and the like, so that the target data group can be determined according to the identifier of the target data.
For example, after determining the target data group, the target data group is searched for the target data, and it is understood that the target data group may include a plurality of data, and the target data is searched for in the plurality of data.
It can be understood that the data is searched by associating the data groups, and the data searching efficiency is effectively improved.
In some embodiments, the method further comprises: in each associated data group, generating a policy data relation tree according to the associated identification corresponding to the common policy data, wherein the policy data relation tree at least comprises two layers; the searching for target data in the target data group comprises: and searching target data in each layer of the list-keeping data relation tree in the target data group.
For example, in the association data group, the policy data relationship tree may be generated according to association identifiers corresponding to a plurality of common policy data, and it may be understood that when the policy data is stored in the policy of the task management system, the relationship includes a storage relationship, for example, in a vehicle policy, a vehicle and a passenger both have corresponding different items to be guaranteed, the vehicle has a plurality of items to be guaranteed, the passenger has a plurality of items to be guaranteed, and there is a relationship between them, and after the common policy data is classified into the association data group, the policy data relationship tree including at least two layers is established according to the association identifiers, and it may be understood that the association identifiers may include a relationship between the common policy data, such as an inclusion relationship or a parallel relationship.
Illustratively, in response to a data acquisition instruction, a classification query method based on artificial intelligence, for example, including keyword extraction and comparison of a keyword model of a neural network, may search for target data in each layer of a policy data relationship tree in an associated data group, which may effectively improve the rate of searching for data.
In some embodiments, the finding target data in each level of the policy data relationship tree in the target data group includes: searching target data in data corresponding to the Nth layer of the policy data relation tree, wherein N is a natural number greater than 0 and is not greater than the total number of layers of the data relation tree;
if the target data cannot be found in the data corresponding to the Nth layer of the policy data relationship tree, and if N is less than the total number of layers of the policy data relationship tree, adding 1 to N;
and if the target data is found in the data corresponding to the nth layer of the policy data relation tree, acquiring the target data and the attribute of the nth layer of the policy data relation tree.
For example, when searching for target data, the policy data key tree may be searched layer by layer, for example, starting from the first layer of the policy data key tree, that is, searching in the root node of the policy data key tree, and if the target data cannot be found in the first layer of the policy data key tree, entering the second layer of the policy data key tree to continue searching, that is, searching in the child node of the root node of the policy data key tree.
For example, if the target data is found at the nth layer of the policy data key tree, the target data and the attribute of the nth layer of the policy data relationship tree are obtained, where the attribute of the nth layer of the policy data relationship tree may be used to indicate the name of the nth layer of the policy data relationship tree, for example, the name of the first layer of the policy data relationship tree is a fee, and the name of the child node of the second layer includes a premium fee, a reimbursement fee, and the like.
For example, if the target data cannot be found when N is added up to equal to the total number of layers of the policy data key tree, it is determined that the target data does not belong to the common policy data. For example, when the leaf node of the policy data key tree is reached, the target data cannot be found, and it is determined that the target data to be found is not the common policy data, that is, the target data to be found does not exist in at least two policies but exists in one policy.
And when the target data are judged not to belong to the common policy data, searching the target data in the policy data in all policies corresponding to the task information.
And S106, sending the target data to the client.
Illustratively, after the target data is found, the target data is output to the client, so that the user/insurance staff can know the target data.
For example, a data association node corresponding to the target data may also be output to the client, for example, an association data group where the target data is located, and/or an attribute of an nth layer of a policy data relationship tree where the target data is located. It can be understood that the data association node corresponding to the target data is output, so that the user/the insurance carrier can obtain more comprehensive information, and the use experience of the user is improved.
In some embodiments, the method further comprises: acquiring a data change instruction, wherein the data change instruction comprises target change data and change data corresponding to the target change data; searching target change data in the data correlation node; modifying the target modification data based on modification data.
For example, a data modification instruction is obtained, where the data modification instruction includes target modification data and modification data corresponding to the target modification data, and whether the target modification data is included is searched for in the data association node, for example, the target modification data is searched for in each association data group, and if the target modification data is searched for in the data association node, the target modification data may be modified based on the modification data.
For example, if the target change data cannot be searched in the data association node, the target change data is searched in all policy data corresponding to the policy.
For example, after the target modified data is modified, if it is determined that the modified data does not belong to the common policy data, the corresponding data association node is modified, for example, the modified data and the data associated with the modified data are moved out of the association data group. It is understood that before the data is not changed, the data may exist in both the a policy and the B policy, and when the policy data of one of the policies is changed, the changed policy data is different from the policy data of the other policy, both of which do not belong to the common policy data, and the changed policy data and the corresponding policy data of the other policy are shifted out of the associated data set.
Illustratively, the data association node searches for data to be changed and changes the data, so that the data change rate of the insurance task can be effectively improved.
In some embodiments, the method further comprises: when the policy data is output to the client, judging whether the policy data is of a first data type, wherein the first data type consists of digital characters; and if the policy data is of a first data type, performing data type transformation on the policy data of the first data type based on a data transformation model to obtain policy data of a second data type, wherein the second data type comprises currency symbols and digit separators.
For example, when the data type of the policy data displayed by the client may be different from the data type required by the server for processing, the policy data output from the server to the client may be processed through the data transformation model, or the policy data acquired by the server from the client may be processed through the data transformation model, so that the user can see the desired data type at the client and the data type can be unified and the processing speed can be increased when the server performs data processing.
Illustratively, the data transformation model may be used to alter the data type of the policy data, such as when the policy data is a premium or a premium, an amount may need to be displayed in the client, such as an amount including a number, a digit delimiter and a currency symbol, but may be in a form including only a number in the server, and when the digital combined premium or premium is output at the server, the data transformation model converts the digital combined premium or premium into a premium or premium in the form of a currency symbol and a digit delimiter for display in the client, making it easier for the user/insurer to read the premium or premium.
Specifically, the data transformation model may be obtained from the back of the policy data, the digit separator is inserted when the nm-th bit of the policy data is obtained, and the insertion operation of the digit separator is completed when n +1 is less than d until nm is greater than or equal to d. Wherein n is a positive integer greater than 0, m is a preset value greater than 0, and d is a positive integer greater than 0, and is used for indicating the total digits of the policy data. The data transformation model may also derive currency symbols from the policy data corresponding to the policy.
In the policy data query method provided in the above embodiment, the number of policies corresponding to the task information is determined by the task management system, and when the number of policies corresponding to the task information is greater than or equal to the preset value, the same policy data is extracted and the data association node is established, and is searched and modified in the data association node.
Referring to fig. 3, fig. 3 is a schematic diagram of policy data query according to an embodiment of the present application, where the policy data query may be configured in a server or a terminal for executing the policy data processing method.
As shown in fig. 3, the policy data query includes: the system comprises a task information acquisition module 110, an insurance policy determination module 120, a common insurance policy data determination module 130, a data association node establishment module 140, a target data search module 150 and a target data output module 160.
The task information obtaining module 110 is configured to obtain task information sent by a client.
And the policy determining module 120 is configured to determine, based on the task management system, a policy corresponding to the task information according to the task information.
A common policy data determining module 130, configured to determine common policy data according to policy data in all policies corresponding to the task information when the number of the policies corresponding to the task information is greater than or equal to a preset value, where the common policy data is policy data existing in at least two policies, and the preset value is a natural number greater than 1.
And a data association node establishing module 140, configured to establish a data association node according to the common policy data based on a node establishing model.
And the target data searching module 150 is used for responding to the data acquisition instruction and searching the target data in the data association node.
A target data output module 160, configured to send the target data to the client.
Illustratively, the data association node establishing module 140 further includes an association identifier determining sub-module and an association data set determining sub-module.
And the association identifier determining submodule is used for determining the association identifier corresponding to the common policy data based on the identifier network in the node establishment model.
And the association data group determining submodule is used for classifying the common policy data into at least one association data group according to the association identifier so as to complete the establishment of the data association node.
The target data searching module 150 is further configured to obtain an identifier of the target data in the data obtaining instruction; and determining a target data group in the plurality of associated data groups according to the identification of the target data, and searching the target data in the target data group.
Illustratively, the policy data query further includes a policy data relationship tree generation sub-module.
And the policy data relation tree generating submodule is used for generating a policy data relation tree in each associated data group according to the associated identification corresponding to the common policy data, and the policy data relation tree at least comprises two layers.
The target data searching module 150 is further configured to search target data in each layer of the policy data relationship tree in the target data group.
Illustratively, the target data lookup module 150 is further configured to:
searching target data in data corresponding to the Nth layer of the policy data relation tree, wherein N is a natural number greater than 0 and is not greater than the total number of layers of the data relation tree;
if the target data cannot be found in the data corresponding to the Nth layer of the policy data relationship tree, and if N is less than the total number of layers of the policy data relationship tree, adding 1 to N;
and if the target data is found in the data corresponding to the nth layer of the policy data relation tree, acquiring the target data and the attribute of the nth layer of the policy data relation tree.
Illustratively, the common policy data determining module 130 further includes a key-value pair obtaining sub-module and a key-value pair comparing sub-module.
And the key value pair acquisition submodule is used for traversing the policy data in all the policies to obtain the key value pair corresponding to the policy data.
And the key value pair comparison submodule is used for determining the policy data in the multiple policies as common policy data if the key value pairs corresponding to the policy data in the multiple policies are the same.
Illustratively, the policy data query further comprises a keyword extraction module.
And the keyword extraction module is used for extracting a plurality of task keywords from the task information based on a keyword extraction model.
The policy determining module 120 is further configured to determine, in the task management system, a policy corresponding to the task information according to the task keyword.
Illustratively, the policy determination module 120 further includes a task traversal sub-module, a target task determination sub-module, and a policy determination sub-module for the target task.
And the task traversing submodule is used for traversing all tasks in the task management system according to the task keywords and determining the matching degree of the task keywords and each task in the task management system.
And the target task determining submodule is used for determining a target task in the task management system according to the matching degree.
And the policy ensuring sub-module of the target task is also used for determining the policy corresponding to the target task according to a preset mapping relation.
It should be noted that, as will be clear to those skilled in the art, for convenience and brevity of description, the specific working processes of the apparatus, the modules and the units described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The methods of the present application are operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The above-described methods and apparatuses may be implemented, for example, in the form of a computer program that can be run on a computer device as shown in fig. 4.
Referring to fig. 4, fig. 4 is a schematic block diagram of a computer device according to an embodiment of the present disclosure. The computer device may be a server or a terminal.
As shown in fig. 4, the computer device includes a processor, a memory, and a network interface connected by a system bus, wherein the memory may include a storage medium and an internal memory.
The storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause a processor to perform any one of the policy data querying methods.
The processor is used for providing calculation and control capability and supporting the operation of the whole computer equipment.
The internal memory provides an environment for the execution of a computer program on a storage medium, which when executed by the processor causes the processor to perform any of the methods of policy data query.
The network interface is used for network communication, such as sending assigned tasks and the like. Those skilled in the art will appreciate that the architecture shown in fig. 4 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
It should be understood that the Processor may be a Central Processing Unit (CPU), and the Processor may be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, etc. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein, in one embodiment, the processor is configured to execute a computer program stored in the memory to implement the steps of:
acquiring task information sent by a client;
based on a task management system, determining a policy corresponding to the task information according to the task information;
when the number of the policy corresponding to the task information is greater than or equal to a preset value, determining common policy data according to policy data in all the policies corresponding to the task information, wherein the common policy data is policy data at least existing in two policies, and the preset value is a natural number greater than 1;
establishing a data association node according to the common policy data based on a node establishment model;
in response to a data acquisition instruction, searching target data in the data association node;
and sending the target data to the client.
In one embodiment, the processor, in implementing the node-based establishment model to establish the data association node for the common policy data, is configured to implement:
determining an associated identifier corresponding to the common policy data based on an identifier network in the node establishment model;
classifying the common policy data into at least one associated data group according to the associated identification so as to complete the establishment of the data associated node;
the processor, when implementing to respond to a data acquisition instruction and search for target data in the data association node, is configured to implement:
acquiring the identification of the target data in the data acquisition instruction;
and determining a target data group in the plurality of associated data groups according to the identification of the target data, and searching the target data in the target data group.
In one embodiment, the processor, when implementing the policy data query method, is configured to implement:
in each associated data group, generating a policy data relation tree according to the associated identification corresponding to the common policy data, wherein the policy data relation tree at least comprises two layers;
when the processor is used for searching the target data in the target data group, the processor is used for realizing that:
and searching target data in each layer of the list-keeping data relation tree in the target data group.
In one embodiment, the processor, when implementing searching for target data in each level of a single data relationship tree in the target association data set, is configured to implement:
searching target data in data corresponding to the Nth layer of the policy data relation tree, wherein N is a natural number greater than 0 and is not greater than the total number of layers of the data relation tree;
if the target data cannot be found in the data corresponding to the Nth layer of the policy data relationship tree, and if N is less than the total number of layers of the policy data relationship tree, adding 1 to N;
and if the target data is found in the data corresponding to the nth layer of the policy data relation tree, acquiring the target data and the attribute of the nth layer of the policy data relation tree.
In one embodiment, when determining common policy data according to policy data in all policies corresponding to the task information, the processor is configured to:
traversing policy data in all policies to obtain key value pairs corresponding to the policy data;
and if the corresponding key value pairs of the policy data in the multiple policies are the same, determining the policy data in the multiple policies as common policy data.
In one embodiment, the processor, when implementing the policy data query method, is configured to implement:
extracting a plurality of task keywords from the task information based on a keyword extraction model;
when the processor is used for determining the number of the insurance policies corresponding to the task information according to the task information based on the task management system, the processor is used for realizing that:
and determining the number of insurance policies corresponding to the task information in the task management system according to the task keywords.
In one embodiment, when determining the number of policies corresponding to the task in the task management system according to the task keyword, the processor is configured to:
traversing all tasks in the task management system according to the task keywords, and determining the matching degree of the task keywords and each task in the task management system;
determining a target task in the task management system according to the matching degree;
and determining the policy corresponding to the target task according to a preset mapping relation.
It should be noted that, as will be clear to those skilled in the art, for convenience and brevity of description, the specific working process of the policy data query described above may refer to the corresponding process in the foregoing policy data query control method embodiment, and details are not described herein again.
Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, where the computer program includes program instructions, and a method implemented when the program instructions are executed may refer to various embodiments of the policy data query method in the present application.
The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the computer device.
It is to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments. While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A policy data query method, the method comprising:
acquiring task information sent by a client;
based on a task management system, determining a policy corresponding to the task information according to the task information;
when the number of the policy corresponding to the task information is greater than or equal to a preset value, determining common policy data according to policy data in all the policies corresponding to the task information, wherein the common policy data is policy data at least existing in two policies, and the preset value is a natural number greater than 1;
establishing a data association node according to the common policy data based on a node establishment model;
in response to a data acquisition instruction, searching target data in the data association node;
and sending the target data to the client.
2. The policy data query method according to claim 1, wherein said establishing a data association node for said common policy data based on a node-established model comprises:
determining an associated identifier corresponding to the common policy data based on an identifier network in the node establishment model;
classifying the common policy data into at least one associated data group according to the associated identification so as to complete the establishment of the data associated node;
the searching target data in the data association node in response to the data acquisition instruction comprises:
acquiring the identification of the target data in the data acquisition instruction;
and determining a target data group in the plurality of associated data groups according to the identification of the target data, and searching the target data in the target data group.
3. The policy data query method of claim 2, wherein said method further comprises:
in each associated data group, generating a policy data relation tree according to the associated identification corresponding to the common policy data, wherein the policy data relation tree at least comprises two layers;
the searching for target data in the target data group comprises:
and searching target data through each layer of the list-keeping data relation tree in the target data group.
4. The policy data query method according to claim 3, wherein said searching for target data in each level of the policy data relationship tree in said target association data set comprises:
searching target data in data corresponding to the Nth layer of the policy data relation tree, wherein N is a natural number greater than 0 and is not greater than the total number of layers of the data relation tree;
if the target data cannot be found in the data corresponding to the Nth layer of the policy data relationship tree, and if N is less than the total number of layers of the policy data relationship tree, adding 1 to N;
and if the target data is found in the data corresponding to the nth layer of the policy data relation tree, acquiring the target data and the attribute of the nth layer of the policy data relation tree.
5. The policy data query method according to any one of claims 1 to 4, wherein the determining common policy data from policy data in all policies corresponding to the task information comprises:
traversing policy data in all policies to obtain key value pairs corresponding to the policy data;
and if the corresponding key value pairs of the policy data in the multiple policies are the same, determining the policy data in the multiple policies as common policy data.
6. The policy data query method according to any one of claims 1-4, wherein the method further comprises:
extracting a plurality of task keywords from the task information based on a keyword extraction model;
the task-based management system determines the number of insurance policies corresponding to the task information according to the task information, and comprises the following steps:
and determining the number of insurance policies corresponding to the task information in the task management system according to the task keywords.
7. The policy data query method according to claim 6, wherein the determining the number of policies corresponding to the task in the task management system according to the task keyword comprises:
traversing all tasks in the task management system according to the task keywords, and determining the matching degree of the task keywords and each task in the task management system;
determining a target task in the task management system according to the matching degree;
and determining the policy corresponding to the target task according to a preset mapping relation.
8. A policy data query device, the policy data query device comprising:
the task information acquisition module is used for acquiring task information sent by the client;
the policy determining module is used for determining a policy corresponding to the task information according to the task information based on the task management system;
a common policy data determining module, configured to determine common policy data according to policy data in all policies corresponding to the task information when the number of the policies corresponding to the task information is greater than or equal to a preset value, where the common policy data is policy data existing in at least two policies, and the preset value is a natural number greater than 1;
the data association node establishing module is used for establishing a data association node according to the common policy data based on a node establishing model;
the target data searching module is used for responding to a data acquisition instruction and searching target data in the data correlation node;
and the target data output module is used for sending the target data to the client.
9. A computer device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, implements the steps of the policy data query method according to any one of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon a computer program, wherein the computer program, when being executed by a processor, carries out the steps of the policy data query method according to any one of claims 1 to 7.
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